25 June, 2019
library(ggplot2)## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
library(dplyr)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(readr) # Functions for importing & exporting filesR can read files from your computer, or it can use a weblink to a file. Today we’ll read in this file I’ve put up on github so it’s easier to distribute to all of you. If you wanted to read in your own data, you would give R the path to your file on your machine.
## Parsed with column specification:
## cols(
## Alice = col_character(),
## `20` = col_double(),
## Blue = col_character()
## )
## # A tibble: 4 x 3
## Alice `20` Blue
## <chr> <dbl> <chr>
## 1 Bob 18 Red
## 2 Claire 23 Blue
## 3 Doug 21 Orange
## 4 Elizabeth 18 Green
# These are 2 different data files hosted on Github. R can stream files from web links (not usually best way to get data).
people_link <- "https://raw.githubusercontent.com/UNC-HLC-R-Intro/lessons_2019/master/data_import/people.csv"
people2_link <- "https://raw.githubusercontent.com/UNC-HLC-R-Intro/lessons_2019/master/data_import/people2.csv"people <- read_csv(people_link)## Parsed with column specification:
## cols(
## name = col_character(),
## age = col_double(),
## favorite_color = col_character()
## )
people## # A tibble: 5 x 3
## name age favorite_color
## <chr> <dbl> <chr>
## 1 Alice 20 Blue
## 2 Bob 18 Red
## 3 Claire 23 Blue
## 4 Doug 21 Orange
## 5 Elizabeth 18 Green
read_csv(people2_link)## Parsed with column specification:
## cols(
## `# This dataset contains information about people` = col_character(),
## `their ages` = col_double(),
## `and their favorite colors` = col_character()
## )
## # A tibble: 5 x 3
## `# This dataset contains information… `their ages` `and their favorite c…
## <chr> <dbl> <chr>
## 1 Alice 20 Blue
## 2 Bob 18 Red
## 3 Claire 23 Blue
## 4 Doug 21 Orange
## 5 Elizabeth 18 Green
people_renamed <- people %>%
rename(color = favorite_color)readr has several write_<type> functions.
write_csv(people_renamed, path = "people_renamed.csv", col_names = T)write_delim is a generic writer that allows you to set the delimiter.
## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character(),
## disease = col_character()
## )
## See spec(...) for full column specifications.
## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character(),
## disease = col_character()
## )
## See spec(...) for full column specifications.
tx_measles_link <- "https://raw.githubusercontent.com/UNC-HLC-R-Intro/lessons_2019/master/data/tycho_tx_measles_wide.csv"tx_measles <- read_csv(tx_measles_link)## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character(),
## disease = col_character()
## )
## See spec(...) for full column specifications.
This dataset tracks disease occurrance over time
tx_measles ## # A tibble: 1 x 4,323
## state disease `192801` `192802` `192803` `192804` `192805` `192806`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TEXAS MEASLES 1.18 0.63 0.62 0.37 1.57 3.44
## # … with 4,315 more variables: `192807` <dbl>, `192808` <dbl>,
## # `192809` <dbl>, `192810` <dbl>, `192811` <dbl>, `192812` <dbl>,
## # `192813` <dbl>, `192814` <dbl>, `192815` <dbl>, `192816` <dbl>,
## # `192817` <dbl>, `192818` <dbl>, `192819` <dbl>, `192820` <dbl>,
## # `192821` <dbl>, `192822` <dbl>, `192823` <dbl>, `192824` <dbl>,
## # `192825` <dbl>, `192826` <dbl>, `192827` <dbl>, `192828` <dbl>,
## # `192829` <dbl>, `192830` <dbl>, `192831` <dbl>, `192832` <dbl>,
## # `192833` <dbl>, `192834` <dbl>, `192835` <dbl>, `192836` <dbl>,
## # `192837` <dbl>, `192838` <dbl>, `192839` <dbl>, `192840` <dbl>,
## # `192841` <dbl>, `192842` <dbl>, `192843` <dbl>, `192844` <dbl>,
## # `192845` <dbl>, `192846` <dbl>, `192847` <dbl>, `192848` <dbl>,
## # `192849` <dbl>, `192850` <dbl>, `192851` <dbl>, `192852` <dbl>,
## # `192901` <dbl>, `192902` <dbl>, `192903` <dbl>, `192904` <dbl>,
## # `192905` <dbl>, `192906` <dbl>, `192907` <dbl>, `192908` <dbl>,
## # `192909` <dbl>, `192910` <dbl>, `192911` <dbl>, `192912` <dbl>,
## # `192913` <dbl>, `192914` <dbl>, `192915` <dbl>, `192916` <dbl>,
## # `192917` <dbl>, `192918` <dbl>, `192919` <dbl>, `192920` <dbl>,
## # `192921` <dbl>, `192922` <dbl>, `192923` <dbl>, `192924` <dbl>,
## # `192925` <dbl>, `192926` <dbl>, `192927` <dbl>, `192928` <dbl>,
## # `192929` <dbl>, `192930` <dbl>, `192931` <dbl>, `192932` <dbl>,
## # `192933` <dbl>, `192934` <dbl>, `192935` <dbl>, `192936` <dbl>,
## # `192937` <dbl>, `192938` <dbl>, `192939` <dbl>, `192940` <dbl>,
## # `192941` <dbl>, `192942` <dbl>, `192943` <dbl>, `192944` <dbl>,
## # `192945` <dbl>, `192946` <dbl>, `192947` <dbl>, `192948` <dbl>,
## # `192949` <dbl>, `192950` <dbl>, `192951` <dbl>, `192952` <dbl>,
## # `193001` <dbl>, `193002` <dbl>, …
How would you make a plot of these data?
library(reshape2)Converting data from wide to long (sometimes called “tidy”) format is called a “melt” or a “pivot”.
The reshape2 library allows us to melt data
melt(tx_measles) ## Using state, disease as id variables
## state disease variable value
## 1 TEXAS MEASLES 192801 1.18
## 2 TEXAS MEASLES 192802 0.63
## 3 TEXAS MEASLES 192803 0.62
## 4 TEXAS MEASLES 192804 0.37
## 5 TEXAS MEASLES 192805 1.57
## 6 TEXAS MEASLES 192806 3.44
Read the help documentation for melt. How can you set the names of the
variable and value columns?
Hint: melt has different behavior for different data types, you
might have to go to a second help page under “details”.
## state disease yearweek incidence
## 1 TEXAS MEASLES 192801 1.18
## 2 TEXAS MEASLES 192802 0.63
## 3 TEXAS MEASLES 192803 0.62
## 4 TEXAS MEASLES 192804 0.37
## 5 TEXAS MEASLES 192805 1.57
## 6 TEXAS MEASLES 192806 3.44
tycho_link <- "https://raw.githubusercontent.com/UNC-HLC-R-Intro/lessons_2019/master/data/tycho_wide_time-disease.csv"tycho <- read_csv(tycho_link)## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character()
## )
## See spec(...) for full column specifications.
This dataset tracks diseases over time
head(tycho)## # A tibble: 6 x 15,548
## state `192801_MEASLES` `192801_POLIO` `192801_SMALLPO… `192802_MEASLES`
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ALAB… 3.67 0 0.04 6.25
## 2 ALAS… 0 0 0 0
## 3 ARIZ… 1.9 0 0 6.4
## 4 ARKA… 4.11 0 0.38 9.91
## 5 CALI… 1.38 0.17 0.34 1.8
## 6 COLO… 8.38 0.39 3.06 6.02
## # … with 15,543 more variables: `192802_POLIO` <dbl>,
## # `192802_SMALLPOX` <dbl>, `192803_MEASLES` <dbl>, `192803_POLIO` <dbl>,
## # `192803_SMALLPOX` <dbl>, `192804_MEASLES` <dbl>, `192804_POLIO` <dbl>,
## # `192804_SMALLPOX` <dbl>, `192805_MEASLES` <dbl>, `192805_POLIO` <dbl>,
## # `192805_SMALLPOX` <dbl>, `192806_MEASLES` <dbl>, `192806_POLIO` <dbl>,
## # `192806_SMALLPOX` <dbl>, `192807_MEASLES` <dbl>, `192807_POLIO` <dbl>,
## # `192807_SMALLPOX` <dbl>, `192808_MEASLES` <dbl>, `192808_POLIO` <dbl>,
## # `192808_SMALLPOX` <dbl>, `192809_MEASLES` <dbl>, `192809_POLIO` <dbl>,
## # `192809_SMALLPOX` <dbl>, `192810_MEASLES` <dbl>, `192810_POLIO` <dbl>,
## # `192810_SMALLPOX` <dbl>, `192811_MEASLES` <dbl>, `192811_POLIO` <dbl>,
## # `192811_SMALLPOX` <dbl>, `192812_MEASLES` <dbl>, `192812_POLIO` <dbl>,
## # `192812_SMALLPOX` <dbl>, `192813_MEASLES` <dbl>, `192813_POLIO` <dbl>,
## # `192813_SMALLPOX` <dbl>, `192814_MEASLES` <dbl>, `192814_POLIO` <dbl>,
## # `192814_SMALLPOX` <dbl>, `192815_MEASLES` <dbl>, `192815_POLIO` <dbl>,
## # `192815_SMALLPOX` <dbl>, `192816_MEASLES` <dbl>, `192816_POLIO` <dbl>,
## # `192816_SMALLPOX` <dbl>, `192817_MEASLES` <dbl>, `192817_POLIO` <dbl>,
## # `192817_SMALLPOX` <dbl>, `192818_MEASLES` <dbl>, `192818_POLIO` <dbl>,
## # `192818_SMALLPOX` <dbl>, `192819_MEASLES` <dbl>, `192819_POLIO` <dbl>,
## # `192819_SMALLPOX` <dbl>, `192820_MEASLES` <dbl>, `192820_POLIO` <dbl>,
## # `192820_SMALLPOX` <dbl>, `192821_MEASLES` <dbl>, `192821_POLIO` <dbl>,
## # `192821_SMALLPOX` <dbl>, `192822_MEASLES` <dbl>, `192822_POLIO` <dbl>,
## # `192822_SMALLPOX` <dbl>, `192823_MEASLES` <dbl>, `192823_POLIO` <dbl>,
## # `192823_SMALLPOX` <dbl>, `192824_MEASLES` <dbl>, `192824_POLIO` <dbl>,
## # `192824_SMALLPOX` <dbl>, `192825_MEASLES` <dbl>, `192825_POLIO` <dbl>,
## # `192825_SMALLPOX` <dbl>, `192826_MEASLES` <dbl>, `192826_POLIO` <dbl>,
## # `192826_SMALLPOX` <dbl>, `192827_MEASLES` <dbl>, `192827_POLIO` <dbl>,
## # `192827_SMALLPOX` <dbl>, `192828_MEASLES` <dbl>, `192828_POLIO` <dbl>,
## # `192828_SMALLPOX` <dbl>, `192829_MEASLES` <dbl>, `192829_POLIO` <dbl>,
## # `192829_SMALLPOX` <dbl>, `192830_MEASLES` <dbl>, `192830_POLIO` <dbl>,
## # `192830_SMALLPOX` <dbl>, `192831_MEASLES` <dbl>, `192831_POLIO` <dbl>,
## # `192831_SMALLPOX` <dbl>, `192832_MEASLES` <dbl>, `192832_POLIO` <dbl>,
## # `192832_SMALLPOX` <dbl>, `192833_MEASLES` <dbl>, `192833_POLIO` <dbl>,
## # `192833_SMALLPOX` <dbl>, `192834_MEASLES` <dbl>, `192834_POLIO` <dbl>,
## # `192834_SMALLPOX` <dbl>, `192835_MEASLES` <dbl>, `192835_POLIO` <dbl>,
## # …
Tidy this dataset with reshape2
library(tidyr)##
## Attaching package: 'tidyr'
## The following object is masked from 'package:reshape2':
##
## smiths